Senior AI and ML HPC Cluster Engineer

Details

NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world.

As a member of the GPU AI/HPC Infrastructure team, you will provide leadership in the design and implementation of ground breaking GPU compute clusters that run demanding deep learning, high performance computing, and computationally intensive workloads. You will identify architectural changes and new approaches for GPU Compute Clusters, addressing compute, networking, and storage design for large scale, high performance workloads, resource utilization in heterogeneous compute environments, private/public cloud strategy, capacity modeling, and global growth planning.

Responsibilities

• Provide leadership and strategic guidance on the management of large-scale HPC systems including deployment of compute, networking, and storage.
• Develop and improve the ecosystem around GPU-accelerated computing, including developing scalable automation solutions.
• Build and maintain AI and ML heterogeneous clusters on-premises and in the cloud.
• Create and cultivate customer and cross-team relationships to reliably sustain clusters and meet evolving user needs.
• Support researchers to run their workloads, including performance analysis and optimizations.
• Conduct root cause analysis and suggest corrective actions; proactively find and fix issues before they occur.

Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering or related field, or equivalent experience.
• Minimum 5+ years of experience designing and operating large scale compute infrastructure.
• Experience with AI/HPC job schedulers such as Slurm, Kubernetes, PBS, RTDA or LSF.
• Proficient administering CentOS/RHEL and/or Ubuntu Linux distributions.
• Solid understanding of cluster configuration management tools such as Ansible, Puppet, Salt.
• In-depth understanding of container technologies like Docker, Singularity, Podman, Shifter, Charliecloud.
• Proficiency in Python programming and bash scripting.
• Applied experience with AI/HPC workflows that use MPI.
• Experience analyzing and tuning performance for a variety of AI/HPC workloads.
• Passion for continual learning and staying ahead of emerging technologies and approaches in HPC and AI/ML infrastructure.

Ways to stand out

• Background with NVIDIA GPUs, CUDA programming, NCCL and MLPerf benchmarking.
• Experience with machine learning and deep learning concepts, algorithms and models.
• Familiarity with InfiniBand, IPoIB and RDMA.
• Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads.
• Familiarity with deep learning frameworks such as PyTorch and TensorFlow.

Compensation & Benefits

• Base salary ranges (determined by location, experience, and comparable pay):
  • Level 3: 152,000 USD - 241,500 USD per year
  • Level 4: 184,000 USD - 287,500 USD per year
• You will also be eligible for equity and benefits (see NVIDIA benefits page).

Other information

• Applications for this job will be accepted at least until April 28, 2026.
• This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.
• NVIDIA is an equal opportunity employer and is committed to fostering a diverse work environment.